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Context Fusion: Dealing with Sensor Reliability

Context Fusion: Dealing with Sensor Reliability. Christos Anagnostopoulos Odysseas Sekkas Stathes Hadjiefthymiades. Pervasive Computing Research Group, http://p-comp.di.uoa.gr Department of Informatics and Telecommunications University of Athens, Greece.

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Context Fusion: Dealing with Sensor Reliability

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  1. Context Fusion: Dealing with Sensor Reliability Christos Anagnostopoulos Odysseas Sekkas Stathes Hadjiefthymiades Pervasive Computing Research Group, http://p-comp.di.uoa.gr Department of Informatics and Telecommunications University of Athens, Greece SensorFusion07, 08.10.2007, Pisa, Italy

  2. Context Fusion • Context Estimation is characterized by imprecise knowledge (e.g. missing information and unreliability of sources) • Context Fusion is the method of deriving high-level context from low-level, inaccurate sensor data. • Context Fusion Engine • Dynamic Bayesian Networks (DBN) and Fuzzy Logic • incorporates the reliability of sources • more accurate inference on the current user situation i.e., a set of aggregated pieces of context.

  3. Reliability of Sources The context-determination rule that concludes a situationp w.r.t, reliability of the sources: [(a1is u1) andconf1]  …  [(anis un) andconfn] (pis u) confidence value confidence value ai= contextual ingredient (attribute) ui = value confi= confidence of sensor readings on measuring ui

  4. Probabilistic Fusion • Random variables of the DBN are • attributes a (i.e., sensor readings) • situation p (i.e. location of the user, actions, etc.)

  5. Probabilistic Fusion The calculation of conditional probabilities determines the value of the situation at time t i.e., p = p(t) Fusion: find the situation p(t) that maximizes P(p(t))

  6. Fuzzy Probabilistic Fusion Fuzzy Probabilistic Fusion result v* Fuzzy Inference confp, v Confidence Probabilistic Fusion p situation Determination Rule … a2 a1 aN … Probabilistic Fusion results conf1, v*1 conf2, v*2 confN, v*N

  7. Fuzzy Sets for Confidence Fuzzy Values for P(p(t)), confp and confidence probability P*(p(t)) denoted as Linguistic Terms.

  8. Fuzzy Inference Rules ifP(p(t))is lowthenP*(p(t)) is low ifP(p(t))is mediumandconfpis low thenP*(p(t)) is very low ifP(p(t))is mediumandconfpis high thenP*(p(t)) is somewhat high ifP(p(t))is highandconfpis lowthenP*(p(t)) is medium ifP(p(t))is highandconfpis highthenP*(p(t)) is high

  9. System Evaluation We assume that situation p(t) is the location L of the user at time t,L = {meeting room, entrance,…}

  10. System Evaluation • Test-bed involving two technologies • WLANAccess Points (4) • Infrared Beacons (5) • Test-bed area: UoA, Dept. of Informatics and Telecommunications

  11. Reliability of Sources Probability distribution for the sensor AP1 P(AP1=v1|L=L1)=0.5 Reliability (h) for each sensor (A=Access Point (AP), B = IR Beacon) • IR-Beacons appear more reliable on location estimation than WLAN APs • IR-Beacons have shorter range of emission thus improving the accuracy of the estimated location

  12. Mean Confidence Probability

  13. Thank you http://p-comp.di.uoa.gr

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